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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

arXiv

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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A Survey on Fairness in Large Language Models

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Song, Rui Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. Considering the influence of parameter magnitude and training paradigm on research strategy, we divide existing fairness research into oriented to medium-sized LLMs under pre-training and fine-tuning paradigms and oriented to large-sized LLMs under prompting paradigms. First, for medium-sized LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-sized LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey

arXiv

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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Liu, Jiamou Zhang, Ziang Li, Meng Zhu, Liehuang Russello, Giovanni The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China The School of Computer Science The University of Auckland Auckland New Zealand Southeast Institute of Information Technology Beijing Institute of Technology Fujian China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China

With the rapid advancement of artificial intelligence technology, the usage of machine learning models is gradually becoming part of our daily lives. High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power. However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally. This has led them to explore alternative approaches such as outsourced learning and federated learning. While these methods address the feasibility of model training effectively, they introduce concerns about the trustworthiness of the training process since computations are not performed locally. Similarly, there are trustworthiness issues associated with outsourced model inference. These two problems can be summarized as the trustworthiness problem of model computations: How can one verify that the results computed by other participants are derived according to the specified algorithm, model, and input data? To address this challenge, verifiable machine learning (VML) has emerged. This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology. We first analyze the potential verifiability issues that may exist in different machine learning scenarios. Subsequently, we provide a formal definition of ZKP-VML. We then conduct a detailed analysis and classification of existing works based on their technical approaches. Finally, we discuss the key challenges and future directions in the field of ZKP-based VML. © 2023, CC BY.

关键词： Machine learning

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Sparse Training data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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Beam Tracking for High-Speed UAV Via Generative Diffusion Model-Enabled Joint Optimization Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Jing Zhang, Chaofeng Feng, Xin Yang, Hongwei Liang, Shuang Sun, Geng Wang, Jiacheng Niyato, Dusit Changchun University of Science and Technology College of Computer Science and Technology Changchun130022 China Jilin University College of Communication Engineering Changchun130012 China Northeast Normal University School of Information Science and Technology Changchun130117 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Nanyang Technological University 639798 Singapore

Beam tracking is crucial for maintaining stable data transmission in unmanned aerial vehicle (UAV) communications. However, a communication link can be disrupted by frequent switching of narrow beams between a base station and a UAV at certain moments. In this study, we propose a position prediction-based beam tracking algorithm with adaptive beam reconstruction (PPBT-AR) for high-speed UAV. Specifically, long short-term memory (LSTM) recurrent neural networks are utilized to predict the nonlinear flight trajectory during high-speed flight. Moreover, we employ the generative diffusion model (GDM) to jointly optimize the beam width and signal strength, thereby reducing the number of beam switches. In addition, we design an adaptive beam reconstruction (ABR) mechanism to mitigate communication interruptions caused by prediction errors. Simulation results demonstrate that the proposed PPBT-AR reduces the number of beam switches by 66.6% and 76.9% in low-speed and high-speed scenarios compared to the traditional phased array beam tracking algorithm, respectively. © 1967-2012 IEEE.

关键词： Long short-term memory

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A key-phrase Extraction Method Based on Multi-size Convolution Windows for Scientific Literatures

A Key-phrase Extraction Method Based on Multi-size Convoluti...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Yuhong Zhang Yuxin Xie Peipei Li Xuegang Hu Ministry of Education Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei Hefei University of Technology Hefei China

ISBN: (纸本)9781665441506

The key-phrase extraction is important for the downstream tasks in natural language process, and has attracted a lot of attention. Compared with other documents, scientific literatures contain many long phrases. Most existing methods perform poor on these literatures. To address this problem, a key-phrase extraction method based on multi-size convolution windows (KE-MCW) is proposed for scientific literatures in this paper. More specifically, in order to represent more contextual information, a convolutional neural network(CNN) with multi-size filters is introduced to map the documents into distributed feature vectors, then each vector can represent different size phrases. Next, in order to determine whether each word is a part of a keyphrase, a deep recurrent neural network is used to mark the role of each word. Finally, the attention mechanism is used to further judge the importance of each phrase. Experimental results show that our proposed method performs better than some competitive methods for technology literatures.

关键词： Cloud computing Recurrent neural networks Convolution Conferences Natural languages Information filters Feature extraction

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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Chinese Control and Decision Conference, CCDC

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 School of Computer Science and Information Engineering Hefei University of Technology Hefei Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Hefei Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology) Heifei

ISBN: (纸本)9781665478977

Road network restoration is an important issue in the post-disaster disposal and rescue, especially when extraordinarily serious natural disasters (e.g., floods and earthquakes) occur. Central to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand nodes. However, most existing work focuses on the activities of the single repair crew in the static road network, which is unable to adapt to the dynamic changes of the road network caused by secondary disasters, such as rock slides and debris flows. Consequently, this work is concentrated on multicrew dynamic scheduling and routing in road network restoration. Specifically, a model of multicrew dynamic scheduling and routing is first presented. Next, the Markov decision process is adopted to construct a decision model for repair crews, based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing strategy. Finally, experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex, dynamic emergency scenarios of post-disaster.

关键词： Schedules Q-learning Roads Heuristic algorithms Maintenance engineering Markov processes Dynamic scheduling

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Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

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arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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PSSL: Self-supervised learning for personalized search with contrastive sampling

arXiv

引用

arXiv 2021年

作者： Zhou, Yujia Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China School of Information Renmin University of China Université de Montréal MontréalQC Canada Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made great progress in extracting personal signals from the query log and learning user representations. However, neural personalized search is extremely dependent on sufficient data to train the user model. data sparsity is an inevitable challenge for existing methods to learn high-quality user representations. Moreover, the overemphasis on final ranking quality leads to rough data representations and impairs the generalizability of the model. To tackle these issues, we propose a Personalized Search framework with Self-supervised Learning (PSSL) to enhance data representations. Specifically, we adopt a contrastive sampling method to extract paired self-supervised information from sequences of user behaviors in query logs. Four auxiliary tasks are designed to pre-train the sentence encoder and the sequence encoder used in the ranking model. They are optimized by contrastive loss which aims to close the distance between similar user sequences, queries, and documents. Experimental results on two datasets demonstrate that our proposed model PSSL achieves state-of-the-art performance compared with existing *** Codes 68P20 Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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